readme

    text_transformer = text_transformer

)

texts = torch.randint(0, 20000, (2, 256))

# get a ton of <sound, text> pairs and train

wavs = torch.randn(2, 1024)

texts = torch.randint(0, 20000, (2, 256))

loss = mulan(wavs, texts)

loss.backward()

# after much training, you can embed sounds and text into a joint embedding space

# for conditioning the audio LM

embeds = mulan.get_audio_latents(wavs)  # during training

embeds = mulan.get_text_latents(texts)  # during inference

```

## Todo